Using Audiometric Data to Weigh and Prioritize Factors that Affect Workers’ Hearing Loss through Support Vector Machine (SVM) Algorithm

Workers’exposure to excessive noise is a big universal work-related challenges.One of the major consequences of exposure to noise is permanent or transient hearing loss.The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’hearing loss based using the Support Vector Machine(SVM)algorithm.This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran.The participating workers(n=150)were divided into three groups of 50 based on the sound pressure level to which they were exposed(two experimental groups and one control group).Audiometric tests were carried out for all members of each group.The study generally entailed the following steps:(1)selecting predicting variables to weigh and prioritize factors affecting hearing loss;(2)conducting audiometric tests and assessing permanent hearing loss in each ear and then evaluating total hearing loss;(3)categorizing different types of hearing loss;(4)weighing and prioritizing factors that affect hearing loss based on the SVM algorithm;and(5)assessing the error rate and accuracy of the models.The collected data were fed into SPSS 18,followed by conducting linear regression and paired samples t-test.It was revealed that,in the first model(SPL<70 dBA),the frequency of 8 KHz had the greatest impact(with a weight of 33%),while noise had the smallest influence(with a weight of 5%).The accuracy of this model was 100%.In the second model(70<SPL<80 dBA),the frequency of 4 KHz had the most profound effect(with a weight of 21%),whereas the frequency of 250 Hz had the lowest impact(with a weight of 6%).The accuracy of this model was 100%too.In the third model(SPL>85 dBA),the frequency of 4 KHz had the highest impact(with a weight of 22%),while the frequency of 250 Hz had the smallest influence(with a weight of 3%).The accuracy of this model was 100%too.In the fourth model,the frequency of 4 KHz had the greatest effect(with a weight of 24%),while the frequency of 500 Hz had the smallest effect(with a weight of 4%).The accuracy of this model was found to be 94%.According to the modeling conducted using the SVM algorithm,the frequency of 4 KHz has the most profound effect on predicting changes in hearing loss.Given the high accuracy of the obtained model,this algorithm is an appropriate and powerful tool to predict and model hearing loss.

Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

CLASSIFICATION OF SKIN AUTOFLUORESCENCE SPECTRUM USING SUPPORT VECTOR MACHINE IN TYPE 2 DIABETES SCREENING

Advanced glycation end products(AGEs)are a complex and heterogencous group of compounds that have been implicated in diabetes related complfcations.Sk in autofluorescence was recently introduced as an altemative tool for skin AGEs accumulation assessment in diabetes.Sucossful optical diagnosis of diabetes requires a rapid and accurate classification algorithm.In order to improve the performance of noninvasive and optical diagnosis of type 2 diabetes,support vector machines(SVM)algorithm was implemented for the clasification of skin autofluorescence from diabetics and control subjects.Cross-validation and grid optimization methods were employed to calculate the optimal parameters that ma ximize classification accuracy.Classification model was set up according to the training set and then veri fied by the testing set.The results show that radical basis fiunction is the best choice in the four common kernels in SVM.Moreover,a diagnostic accuracy of 82.61%,a sensitivity of 69.57%,and a specificity of 95.65%for discriminating diabetics from control subjects were achieved using a mixed kemel function,which is based on liner kernel function and radical basis function.In comparison with fasting plasma glucose and HbAue test,the clasifcation method of skin autofuorescence spectrum based on SVM shows great potential in screening of diabetes.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

Epithelial-mesenchymal transition status of circulating tumor cells in breast cancer and its clinical relevance

Objective:Circulating tumor cells(CTCs)play a critical role in cancer metastasis,but their prevalence and significance remain unclear.This study attempted to track the epithelial-mesenchymal transition(EMT)status of CTCs in breast cancer patients and investigate their clinical relevance.Methods:In this study,the established negFACS-IF:E/M platform was applied to isolate rare CTCs and characterize their EMT status in breast cancer.A total of 89 breast cancer patients were recruited,including stage 0–III(n=60)and late stage(n=29)cases.Results:Using the negFACS-IF:E/M platform,it was found that in human epidermal growth factor receptor 2(HER2)+patients,mesenchymal CTCs usually exhibited a high percentage of HER2+cells.Stage IV breast cancer patients had considerably more CTCs than stage 0–III patients.Among stage 0–III breast cancers,the HER2 subtype included a significantly higher percentage of mesenchymal and biphenotypic(epithelial and mesenchymal)CTCs than the luminal A or B subtypes.Among stage IV patients,CTCs were predominantly epithelial in cases with local recurrence and were more mesenchymal in cases with distant metastasis.By applying a support vector machine(SVM)algorithm,the EMT status of CTCs could distinguish between breast cancer cases with metastasis/local recurrence and those without recurrence.Conclusions:The negFACS-IF:E/M platform provides a flexible and generally acceptable method for the highly sensitive and specific detection of CTCs and their EMT traits in breast cancer.This study demonstrated that the EMT status of CTCs had high clinical relevance in breast cancer,especially in predicting the distant metastasis or local recurrence of breast cancer.